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# Data Engineer

- **Company:** Staffxpert Llc
- **Location:** Houston, TX, United States
- **Experience:** Expert
- **Salary:** $165,000.0 - $194,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Apache HTTP Server, Automation of Tests, Continuous Integration, Data Architecture, Data Discovery, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Security, Database Queries, Graph Database, Interoperability, Python (Programming Language), Metadata, Meta-Data Management, Performance Tuning, DataOps, SPARQL, Enterprise Software Applications, Cloud Platform System, Delivery Pipeline, Snowflake, Git, Data Layers, Data Lineage, Integration Frameworks, Claude, Semantic Modeling, Data Pipelines
- **Published:** September 30, 2026
- **Apply:** https://www.careerjet.com/jobad/us66a8d2448053e1ae92b8dc23ebebf866

## About the Role

The ideal candidate will bring strong expertise in data engineering, architecture, data governance, metadata management, and ontology-driven/semantic data modeling. Experience within the Oil & Gas Midstream domain, along with working knowledge of Claude, is required for this role., Strong experience in Data Engineering, Data Architecture, ETL/ELT, and enterprise data modeling.
Hands-on expertise with Snowflake, including data modeling, performance optimization, access controls, and scalable data platform patterns.
Strong working experience with AWS cloud environments.
Experience with Apache Iceberg or similar open table formats.
Experience with Informatica or comparable enterprise data integration tools.
Hands-on experience with dbt for transformations, testing, documentation, and deployment.
Strong understanding of data governance, cataloging, lineage, metadata management, and policy-driven data access.
Experience or strong understanding of ontologies, semantic modeling, taxonomies, business glossaries, or knowledge graphs.
Strong SQL skills with experience in Python or another data engineering language.
Working knowledge of Claude is required.
Oil & Gas Midstream domain experience is required.
Strong communication, documentation, stakeholder management, and problem-solving skills.
Preferred Qualifications
Experience with Snowflake Catalog and Snowflake Horizon.
Experience building governed data products for analytics, AI, BI, or enterprise applications.
Knowledge of RDF, OWL, SHACL, SPARQL, graph databases, or knowledge graph technologies.
Experience designing semantic layers, metadata models, business glossaries, or domain ontologies.
Familiarity with CI/CD, Git, automated testing, and deployment workflows.
Experience with data observability, data contracts, lineage tracking, and data quality frameworks.
Experience working in complex enterprise environments with multiple data domains and stakeholder groups.

## Description

STAFFXPERT LLC is seeking a Data Engineer / Data Architect on behalf of our client in Houston, Texas. We are looking for an experienced data professional to help design, build, and scale a modern cloud data platform leveraging Snowflake, AWS, Apache Iceberg, Informatica, and dbt., Design, build, and maintain scalable data pipelines across structured, semi-structured, and unstructured data sources.
Architect and optimize enterprise data solutions using Snowflake as a strategic data platform.
Develop ingestion and ETL/ELT workflows using Informatica and transformation models using dbt.
Work with AWS cloud services and infrastructure to support enterprise data products and applications.
Build and manage data architectures using Apache Iceberg, including schema evolution, interoperability, cataloging, and governed access.
Support data governance, metadata management, lineage, data discovery, and policy enforcement using Snowflake Catalog, Snowflake Horizon, and related governance tools.
Develop ontology-driven and semantic data models, including entities, relationships, taxonomies, business glossaries, and semantic mappings.
Translate business requirements into logical and physical data models and reusable data products.
Implement data quality, validation, observability, security, and access-control standards.
Collaborate with architects, governance teams, analysts, application teams, and business stakeholders.
Support data products and use cases across analytics, business intelligence, applications, and AI.